Preisach model of hysteresis for the Piezoelectric Actuator Drive

Zsurzsan, Gabriel; Andersen, Michael A. E.; Zhang, Zhe; Andersen, Nils Axel

doi:10.1109/iecon.2015.7392524

Cited by 10 publications

(3 citation statements)

References 12 publications

(12 reference statements)

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“…The latter group, namely phenomenonbased models, includes three subgroups: differential equation models, operator models, and others. The first two subgroups include Duhem model, 9 Bouc-Wen model, 10 Preisach hysteresis model, 11 and Prandtl-Ishlinskii (PI) hysteresis model. 12 These models try to extract a mathematical law from phenomenon without involving physical principles.…”

Section: Introductionmentioning

confidence: 99%

A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

Zhou

Cao

et al. 2021

International Journal of Advanced Robotic Systems

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Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.

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Section: Introductionmentioning

confidence: 99%

A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

Zhou

Cao

et al. 2021

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Within this method, three different model theories can be distinguished: (1) the theory of dynamic modeling, which is represented mathematically by a collection of differential equations. Some model examples include the Jiles–Atherton model [ 24 ], the Duhem model [ 25 ], the Bouc–Wen model [ 26 ], the Backlash-Like model [ 27 ], the Maxwell model [ 28 ], and the approximated Polynomial model [ 29 ]; (2) the operator modeling theory, including Prandtl–Ishlinskii (PI) [ 30 ], Krasnoselskii–Pokrovskii (KP) [ 31 ], and Preisach [ 32 ] models; (3) the intelligent modeling theory. This approach models systems with hysteresis based on the concept of the computational intelligence [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Uralde

Artetxe

Barambones

et al. 2023

Sensors

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Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.

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“…Following this, Preisach hysteresis modelling was used to model a complete rate-independent hysteresis loop due to its simplicity and calculation speed [26], which proved superior to PI modeling. The Preisach hysteresis model had merit in that it was capable of describing a greater number of hysteresis loop characteristics (i.e., significant and inner loops) [27]. Nevertheless, it was difficult to apply the exact solution of the Preisach hysteresis compensator [28].…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Han,

Xu,

et al. 2023

Preprint

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It is widely known that inchworm piezo motors are being used in ultra-precision detection platforms where high clamping force and nano-precision are required. This paper examines and proposes a dynamic model of the inchworm piezo motor to improve the performance and controller. Traditional identification strategies have not been adopted for calculating parameters for the dynamic model exhibiting hysteresis nonlinearity. In addition, hysteresis deteriorates the accuracy of positioning of inchworm piezo motors. It has been proposed to solve these problems using a feedforward controller, a hysteresis compensator based on a stochastic configuration networks algorithm (SCNs). Using the SCNs algorithm, both voltage and displacement inputs can be used to describe the nonlinear memory of hysteresis, and the kernel function provides a flexible and accurate representation of this behavior. This method enables the dynamic model parameters of the inchworm piezo motor to be identified using frequency sweeps. A proportional-integral (PI) controller is used to verify the positioning accuracy, and experimental results show that an inchworm piezo motor has a positioning accuracy of about 1nm when the laser interference is low with a 10M Hz adoption rate and 0.1nm resolution.

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Preisach model of hysteresis for the Piezoelectric Actuator Drive

Cited by 10 publications

References 12 publications

A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

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